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Souptik Mukherjee Data Science trainer in Delhi

Souptik Mukherjee

Tutor for Data Science/ AI / ML and SAP

East Delhi, Delhi, India - 110092.

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Overview

Physicist | Data Scientist | Teacher | Startup Founder

Hey there! I'm Dr. Souptik Mukherjee, and I've had quite an adventure exploring the world of science and data. I spent 17 years in the USA, where I worked as a scientist and even contributed to big companies that you might know.

Now, I'm back in India, and I'm excited to share what I've learned with you. I teach Data Science, which is like using detective skills to understand information and make smart choices. I can teach you online and in person, if you are residing close by.

But wait, there's more! I also started something cool called Learn Code Quiz. It's not just a regular startup; it's a place where I want to help you learn the basics of Data Science. We'll explore things like computers doing math, cool programs, and how to use data to solve problems.

Languages Spoken

Bengali Mother Tongue (Native)

English Proficient

Hindi Proficient

Education

KENT STATE UNIVERSITY 2016

Doctor of Philosophy (Ph.D.)

Address

East Delhi, Delhi, India - 110092

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Teaches

Data Science Classes

Class Location

Online Classes (Video Call via UrbanPro LIVE)

Student's Home

Tutor's Home

Years of Experience in Data Science Classes

17

Data science techniques

Artificial Intelligence, Machine learning, Python

Teaching Experience in detail in Data Science Classes

Hi, If you are looking for Data Science help using Python, I have some solutions for you. But before that let me introduce myself. I am a Data Scientist, a Physicist and start-up founder. I spent 17 years in the United States, having done two masters, PhD and worked in fortune 500 companies. Right now I am heading a startup Learn Code Quiz in India. We execute client projects and also teach students Python, Data Science in collaboration with our parent company American Software Consulting Group. I will be happy to assist you based on my experiences gathered over the years. These are the modules I teach Module 1: Beginning Your Data Science Journey Setting Up Your Python Environment Installing Jupyter Notebook The Importance of Learning Data Science Understanding the Field of Data Science Essential Tools for Data Science Module 2: Harnessing Python and Jupyter's Power Working with Python Data Types and Operators Getting Familiar with Jupyter Notebook Exploring Basic Data Types Understanding Comparison and Logical Operators Module 3: Exploring Lists, Tuples, and Dictionaries Lists and How to Use Indexing Advanced Indexing Techniques Modifying Data Within Lists Introduction to Tuples Getting to Know Python Dictionaries Module 4: Crafting Python Functions Writing Functions in Python Working with Function Arguments Understanding Methods Creating User-Defined Functions Exploring Nested and Lambda Functions Module 5: Working with Loops and Conditionals Using Conditional Statements (If Statements) Implementing While Loops Getting Comfortable with For Loops Looping Through Dictionary Items Module 6: Navigating Data with NumPy and Pandas Introduction to 2D NumPy Arrays Iterating Over NumPy Arrays Creating DataFrames with Pandas Slicing and Filtering DataFrames with Pandas Utilizing NumPy and Pandas for Statistical Analysis Module 7: Manipulating Data with Pandas Importing and Exporting Data Understanding Pandas Objects: Series and DataFrames Common Functionality with Pandas Objects Selecting and Modifying Data with Pandas Combining and Reshaping DataFrames Module 8: Data Visualization with Matplotlib Introduction to Data Visualization Exploring Matplotlib for Plotting Creating Line Plots Generating Bar Plots Crafting Scatter Plots Understanding Histograms Customizing Graphs Exploring Line of Best Fit Delving into Box Plots Analyzing Data with Pair Plots Visualizing Time Series Data Introduction to 3D Plotting Exporting Figures for Sharing Module 9: Exploring Statistics and Probability Quiz on Statistics, Probability, and Linear Algebra Understanding Probability and Statistics Differentiating Probability vs. Statistics in Python Sampling Techniques in Python Exploring Random Variables and Probability Distributions Analyzing Probability Mass and Density Functions Module 10: Statistical Distributions and Hypothesis Testing Overview of Statistical Distributions Exploring the Uniform Distribution Understanding Bernoulli and Binomial Distributions Unveiling the Normal Distribution Investigating Exponential, Poisson, and T Distributions Confidence Intervals and Hypothesis Testing The Data Cleaning Process and Strategies Handling Missing or Duplicate Data Concluding Data Cleaning Tasks Module 11: Introduction to Exploratory Data Analysis Getting Started with Exploratory Data Analysis (EDA) Analyzing Descriptive Statistics, Frequencies, and Averages Understanding Correlation in Data Visualizing Data in EDA Data Preprocessing for Analysis Summary of Exploratory Data Analysis Module 12: Introduction to Linear Algebra Foundations of Linear Algebra Matrices and Vectors Operations with Matrices Dot Product and Cross Product Matrix Multiplication and Division Transposing Matrices Determinants, Inverses, and More Linear Independence and Eigenvalues Singular Value Decomposition (SVD) Principal Component Analysis (PCA) Maximum Likelihood Estimation (MLE) Module 13: Supervised and Unsupervised Machine Learning Overview of Supervised Machine Learning Introduction to Unsupervised Machine Learning The Basics of Data Modeling Multivariate Data Analysis with Gaussian Distributions Understanding Probabilistic Models Linear Regression: A Foundational Technique Practical Example of Linear Regression Insight into Least Squares Expanding the Scope of Linear Regression Module 14: Understanding Regression Techniques Geometry Behind Least Squares Regression Essential Concepts of Linear Regression Probabilistic Perspective on Linear Regression An Exploration of Probability in Regression Introducing Ridge Regression Unveiling the Role of Regularization The Balance Between Bias and Variance Cross-Validation: A Model Evaluation Technique Bayesian Inference: A Probabilistic Approach Applying Bayesian Concepts to a Coin Toss Example Module 15: Exploring Bayesian Methods The Essence of Bayesian Methods Instructions for Applying Bayesian Principles Bayesian Linear Regression Applications of Posterior Distribution The Concept of Active Learning Analytical Tools for Bayesian Analysis Using Lagrange Multipliers for Optimization Sparse Regression Techniques Insights into Lp Regression Module 16: Introduction to Classification Techniques Understanding Classification Optical Character Recognition with NN Classifier Exploring the K-Nearest Neighbors Classifier Statistical Foundations of Classification Unveiling Optimal Classification Strategies Embracing the Bayes Classifier Gaussian Class Conditional Densities Multivariate Gaussian Classification Plug-In Classifiers: The Practical Approach Linear Classification and Hyperplanes Generalizing Classification to Polynomial Forms Least Squares in Classification Tasks Module 17: Delving into Logistic Regression An In-Depth Look at Logistic Regression The Likelihood in Logistic Regression Unraveling the Logistic Regression Algorithm Laplace Approximation: A Probabilistic Technique Kernel Methods and the World of Gaussian Processes Expanding Features with Kernel Techniques A Comprehensive Study of Kernels Kernelized Perceptron: A Deep Dive Regression with Kernel Functions The Magical World of Gaussian Processes Module 18: The Art of Support Vector Machines Maximum Margin Classifiers: The Foundation Support Vector Machines: A Crucial Tool Primal and Dual Problems in SVM Soft-Margin SVM: Balancing Act An Introduction to Decision Trees Basics of Decision Tree Learning Algorithm The Power of Bootstrapping Bagging and Random Forest: A Closer Look Two Exciting Projects Await Module 19: Boosting and the World of Clustering Boosting: A Technique to Boost Decision Stumps Application Spotlight: Face Detection A Detailed Analysis of Boosting Unsupervised Learning: The Exploration Begins Clustering: Grouping Similar Data Points Understanding the Convergence of K-Means Real-World Applications of K-Means Clustering Module 20: Expectation-Maximization and Beyond Maximum Likelihood: Laying the Groundwork The Expectation-Maximization (EM) Algorithm Navigating the EM Algorithm EM for Handling Missing Data Soft Clustering vs. Hard Clustering Unveiling Gaussian Mixture Models A Closer Look at the M-Step An Example Run with Gaussian Mixtures EM for Generic Mixture Models Two Intriguing Projects Await Module 21: Collaborative Filtering and Topic Modeling Collaborative Filtering: A Puzzle to Solve Matrix Factorization: The Key to Model Inference Probabilistic Matrix Factorization A Dive into Topic Modeling Latent Dirichlet Allocation: Unveiling the Technique Nonnegative Matrix Factorization: Exploring the Concept Dual Objective Functions in NMF and Topic Modeling Module 22: Principal Component Analysis (PCA) Principal Component Analysis: A Dimension Reduction Technique The Fundamentals of PCA The Probabilistic Aspect of PCA The Intricacies of Kernel PCA Personalization through Dimension Reduction Crafting Recommender Systems for Travelers Module 23: Hidden Insights with Markov Models Exploring Markov Models Sequences and Their Significance The Dynamics of Markov Chains The First Order Markov Chain Delving into State and Stationary Distributions A Glimpse into Ranking Algorithms Classification: A Continuing Journey Module 24: Unveiling Hidden Patterns with HMMs and Kalman Filter Understanding Hidden Markov Models Learning the Art of HMM Kalman Filtering: An Algorithmic Marvel The Revisiting of Markov Models Mastering Kalman Filtering Techniques Module 25: Discovering Patterns with Association Analysis The World of Association Analysis and Rules Basket Processing: A Crucial Step Dependencies in Frequency Unearthing Association Rules Selecting Models and Parameters BIC: An Essential Derivative Automated Tracking of Basketball Statistics Module 26: Deep Learning and Text Analysis The Deep Learning Abyss: A Journey Commences The Intricate World of Deep Neural Networks Activation Functions: The Heart of Deep Learning Loss Functions: Understanding Model Errors Gradients and Optimization Techniques The Mathematical Formulations of Deep Neural Networks A Glimpse into Convolutional Neural Networks Handling Pixels, Edges, and Sharpening Images Module 27: Language Secrets: Ciphers, Models, and Analysis The Enigma of Ciphers Language Models: Decoding Text Patterns Sentiment Analysis: Extracting Emotions from Text The Power of Trigrams Building an Article Spinner in Python Thanks and Regards, Dr. Souptik Mukherjee

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FAQs

1. Which classes do you teach?

I teach Data Science Class.

2. Do you provide a demo class?

Yes, I provide a free demo class.

3. How many years of experience do you have?

I have been teaching for 17 years.

Teaches

Data Science Classes

Class Location

Online Classes (Video Call via UrbanPro LIVE)

Student's Home

Tutor's Home

Years of Experience in Data Science Classes

17

Data science techniques

Artificial Intelligence, Machine learning, Python

Teaching Experience in detail in Data Science Classes

Hi, If you are looking for Data Science help using Python, I have some solutions for you. But before that let me introduce myself. I am a Data Scientist, a Physicist and start-up founder. I spent 17 years in the United States, having done two masters, PhD and worked in fortune 500 companies. Right now I am heading a startup Learn Code Quiz in India. We execute client projects and also teach students Python, Data Science in collaboration with our parent company American Software Consulting Group. I will be happy to assist you based on my experiences gathered over the years. These are the modules I teach Module 1: Beginning Your Data Science Journey Setting Up Your Python Environment Installing Jupyter Notebook The Importance of Learning Data Science Understanding the Field of Data Science Essential Tools for Data Science Module 2: Harnessing Python and Jupyter's Power Working with Python Data Types and Operators Getting Familiar with Jupyter Notebook Exploring Basic Data Types Understanding Comparison and Logical Operators Module 3: Exploring Lists, Tuples, and Dictionaries Lists and How to Use Indexing Advanced Indexing Techniques Modifying Data Within Lists Introduction to Tuples Getting to Know Python Dictionaries Module 4: Crafting Python Functions Writing Functions in Python Working with Function Arguments Understanding Methods Creating User-Defined Functions Exploring Nested and Lambda Functions Module 5: Working with Loops and Conditionals Using Conditional Statements (If Statements) Implementing While Loops Getting Comfortable with For Loops Looping Through Dictionary Items Module 6: Navigating Data with NumPy and Pandas Introduction to 2D NumPy Arrays Iterating Over NumPy Arrays Creating DataFrames with Pandas Slicing and Filtering DataFrames with Pandas Utilizing NumPy and Pandas for Statistical Analysis Module 7: Manipulating Data with Pandas Importing and Exporting Data Understanding Pandas Objects: Series and DataFrames Common Functionality with Pandas Objects Selecting and Modifying Data with Pandas Combining and Reshaping DataFrames Module 8: Data Visualization with Matplotlib Introduction to Data Visualization Exploring Matplotlib for Plotting Creating Line Plots Generating Bar Plots Crafting Scatter Plots Understanding Histograms Customizing Graphs Exploring Line of Best Fit Delving into Box Plots Analyzing Data with Pair Plots Visualizing Time Series Data Introduction to 3D Plotting Exporting Figures for Sharing Module 9: Exploring Statistics and Probability Quiz on Statistics, Probability, and Linear Algebra Understanding Probability and Statistics Differentiating Probability vs. Statistics in Python Sampling Techniques in Python Exploring Random Variables and Probability Distributions Analyzing Probability Mass and Density Functions Module 10: Statistical Distributions and Hypothesis Testing Overview of Statistical Distributions Exploring the Uniform Distribution Understanding Bernoulli and Binomial Distributions Unveiling the Normal Distribution Investigating Exponential, Poisson, and T Distributions Confidence Intervals and Hypothesis Testing The Data Cleaning Process and Strategies Handling Missing or Duplicate Data Concluding Data Cleaning Tasks Module 11: Introduction to Exploratory Data Analysis Getting Started with Exploratory Data Analysis (EDA) Analyzing Descriptive Statistics, Frequencies, and Averages Understanding Correlation in Data Visualizing Data in EDA Data Preprocessing for Analysis Summary of Exploratory Data Analysis Module 12: Introduction to Linear Algebra Foundations of Linear Algebra Matrices and Vectors Operations with Matrices Dot Product and Cross Product Matrix Multiplication and Division Transposing Matrices Determinants, Inverses, and More Linear Independence and Eigenvalues Singular Value Decomposition (SVD) Principal Component Analysis (PCA) Maximum Likelihood Estimation (MLE) Module 13: Supervised and Unsupervised Machine Learning Overview of Supervised Machine Learning Introduction to Unsupervised Machine Learning The Basics of Data Modeling Multivariate Data Analysis with Gaussian Distributions Understanding Probabilistic Models Linear Regression: A Foundational Technique Practical Example of Linear Regression Insight into Least Squares Expanding the Scope of Linear Regression Module 14: Understanding Regression Techniques Geometry Behind Least Squares Regression Essential Concepts of Linear Regression Probabilistic Perspective on Linear Regression An Exploration of Probability in Regression Introducing Ridge Regression Unveiling the Role of Regularization The Balance Between Bias and Variance Cross-Validation: A Model Evaluation Technique Bayesian Inference: A Probabilistic Approach Applying Bayesian Concepts to a Coin Toss Example Module 15: Exploring Bayesian Methods The Essence of Bayesian Methods Instructions for Applying Bayesian Principles Bayesian Linear Regression Applications of Posterior Distribution The Concept of Active Learning Analytical Tools for Bayesian Analysis Using Lagrange Multipliers for Optimization Sparse Regression Techniques Insights into Lp Regression Module 16: Introduction to Classification Techniques Understanding Classification Optical Character Recognition with NN Classifier Exploring the K-Nearest Neighbors Classifier Statistical Foundations of Classification Unveiling Optimal Classification Strategies Embracing the Bayes Classifier Gaussian Class Conditional Densities Multivariate Gaussian Classification Plug-In Classifiers: The Practical Approach Linear Classification and Hyperplanes Generalizing Classification to Polynomial Forms Least Squares in Classification Tasks Module 17: Delving into Logistic Regression An In-Depth Look at Logistic Regression The Likelihood in Logistic Regression Unraveling the Logistic Regression Algorithm Laplace Approximation: A Probabilistic Technique Kernel Methods and the World of Gaussian Processes Expanding Features with Kernel Techniques A Comprehensive Study of Kernels Kernelized Perceptron: A Deep Dive Regression with Kernel Functions The Magical World of Gaussian Processes Module 18: The Art of Support Vector Machines Maximum Margin Classifiers: The Foundation Support Vector Machines: A Crucial Tool Primal and Dual Problems in SVM Soft-Margin SVM: Balancing Act An Introduction to Decision Trees Basics of Decision Tree Learning Algorithm The Power of Bootstrapping Bagging and Random Forest: A Closer Look Two Exciting Projects Await Module 19: Boosting and the World of Clustering Boosting: A Technique to Boost Decision Stumps Application Spotlight: Face Detection A Detailed Analysis of Boosting Unsupervised Learning: The Exploration Begins Clustering: Grouping Similar Data Points Understanding the Convergence of K-Means Real-World Applications of K-Means Clustering Module 20: Expectation-Maximization and Beyond Maximum Likelihood: Laying the Groundwork The Expectation-Maximization (EM) Algorithm Navigating the EM Algorithm EM for Handling Missing Data Soft Clustering vs. Hard Clustering Unveiling Gaussian Mixture Models A Closer Look at the M-Step An Example Run with Gaussian Mixtures EM for Generic Mixture Models Two Intriguing Projects Await Module 21: Collaborative Filtering and Topic Modeling Collaborative Filtering: A Puzzle to Solve Matrix Factorization: The Key to Model Inference Probabilistic Matrix Factorization A Dive into Topic Modeling Latent Dirichlet Allocation: Unveiling the Technique Nonnegative Matrix Factorization: Exploring the Concept Dual Objective Functions in NMF and Topic Modeling Module 22: Principal Component Analysis (PCA) Principal Component Analysis: A Dimension Reduction Technique The Fundamentals of PCA The Probabilistic Aspect of PCA The Intricacies of Kernel PCA Personalization through Dimension Reduction Crafting Recommender Systems for Travelers Module 23: Hidden Insights with Markov Models Exploring Markov Models Sequences and Their Significance The Dynamics of Markov Chains The First Order Markov Chain Delving into State and Stationary Distributions A Glimpse into Ranking Algorithms Classification: A Continuing Journey Module 24: Unveiling Hidden Patterns with HMMs and Kalman Filter Understanding Hidden Markov Models Learning the Art of HMM Kalman Filtering: An Algorithmic Marvel The Revisiting of Markov Models Mastering Kalman Filtering Techniques Module 25: Discovering Patterns with Association Analysis The World of Association Analysis and Rules Basket Processing: A Crucial Step Dependencies in Frequency Unearthing Association Rules Selecting Models and Parameters BIC: An Essential Derivative Automated Tracking of Basketball Statistics Module 26: Deep Learning and Text Analysis The Deep Learning Abyss: A Journey Commences The Intricate World of Deep Neural Networks Activation Functions: The Heart of Deep Learning Loss Functions: Understanding Model Errors Gradients and Optimization Techniques The Mathematical Formulations of Deep Neural Networks A Glimpse into Convolutional Neural Networks Handling Pixels, Edges, and Sharpening Images Module 27: Language Secrets: Ciphers, Models, and Analysis The Enigma of Ciphers Language Models: Decoding Text Patterns Sentiment Analysis: Extracting Emotions from Text The Power of Trigrams Building an Article Spinner in Python Thanks and Regards, Dr. Souptik Mukherjee

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Souptik Mukherjee describes himself as Tutor for Data Science/ AI / ML and SAP. He conducts classes in Data Science. Souptik is located in East Delhi, Delhi. Souptik takes Online Classes- via online medium. He has 17 years of teaching experience . Souptik has completed Doctor of Philosophy (Ph.D.) from KENT STATE UNIVERSITY in 2016. He is well versed in English, Bengali and Hindi.

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